Pub Date : 2025-12-29DOI: 10.1088/2058-9565/ae2d8e
Asghar Ullah, Özgür E Müstecaplıoğlu, Matteo G A Paris
We investigate the optimization of graph topologies for quantum sensing networks designed to estimate weak magnetic fields. The sensors are modeled as spin systems governed by a transverse-field Ising Hamiltonian in thermal equilibrium at low temperatures. Using a genetic algorithm (GA), we evolve network topologies to maximize a perturbative spectral sensitivity measure, which serves as the fitness function for the GA. For the best-performing graphs, we compute the corresponding quantum Fisher information (QFI) to assess the ultimate bounds on estimation precision. To enable efficient scaling, we use the GA-generated data to train a deep neural network, allowing extrapolation to larger graph sizes where direct computation becomes prohibitive. Our results show that while both the fitness function and QFI initially increase with system size, the QFI exhibits a clear non-monotonic behavior—saturating and eventually declining beyond a critical graph size. This reflects the loss of superlinear scaling of the QFI, as the narrowing of the energy gap signals a crossover to classical scaling of the QFI with system size. The effect is reminiscent of the microeconomic law of diminishing returns: beyond an optimal graph size, further increases yield reduced sensing performance. This saturation and decline in precision are particularly pronounced under Kac scaling, where both the QFI and spin squeezing plateau or degrade with increasing system size. We also attribute observed even–odd oscillations in the spectral sensitivity measure and QFI to quantum interference effects in spin phase space, as confirmed by our phase-space analysis. These findings highlight the critical role of optimizing interaction topology—rather than simply increasing network size—and demonstrate the potential of hybrid evolutionary and learning-based approaches for designing high-performance quantum sensors.
{"title":"Optimizing quantum sensing networks via genetic algorithms and deep learning","authors":"Asghar Ullah, Özgür E Müstecaplıoğlu, Matteo G A Paris","doi":"10.1088/2058-9565/ae2d8e","DOIUrl":"https://doi.org/10.1088/2058-9565/ae2d8e","url":null,"abstract":"We investigate the optimization of graph topologies for quantum sensing networks designed to estimate weak magnetic fields. The sensors are modeled as spin systems governed by a transverse-field Ising Hamiltonian in thermal equilibrium at low temperatures. Using a genetic algorithm (GA), we evolve network topologies to maximize a perturbative spectral sensitivity measure, which serves as the fitness function for the GA. For the best-performing graphs, we compute the corresponding quantum Fisher information (QFI) to assess the ultimate bounds on estimation precision. To enable efficient scaling, we use the GA-generated data to train a deep neural network, allowing extrapolation to larger graph sizes where direct computation becomes prohibitive. Our results show that while both the fitness function and QFI initially increase with system size, the QFI exhibits a clear non-monotonic behavior—saturating and eventually declining beyond a critical graph size. This reflects the loss of superlinear scaling of the QFI, as the narrowing of the energy gap signals a crossover to classical scaling of the QFI with system size. The effect is reminiscent of the microeconomic law of diminishing returns: beyond an optimal graph size, further increases yield reduced sensing performance. This saturation and decline in precision are particularly pronounced under Kac scaling, where both the QFI and spin squeezing plateau or degrade with increasing system size. We also attribute observed even–odd oscillations in the spectral sensitivity measure and QFI to quantum interference effects in spin phase space, as confirmed by our phase-space analysis. These findings highlight the critical role of optimizing interaction topology—rather than simply increasing network size—and demonstrate the potential of hybrid evolutionary and learning-based approaches for designing high-performance quantum sensors.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"89 1","pages":"015031"},"PeriodicalIF":6.7,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-23DOI: 10.1088/2058-9565/ae2c16
Leander Grech, Matthias G Krauss, Mirko Consiglio, Tony J G Apollaro, Christiane P Koch, Simon Hirlaender and Gianluca Valentino
Noisy intermediate-scale quantum computers hold the promise of tackling complex and otherwise intractable computational challenges through the massive parallelism offered by qubits. Central to realizing the potential of quantum computing are perfect entangling (PE) two-qubit gates, which serve as a critical building block for universal quantum computation. In the context of quantum optimal control, shaping electromagnetic pulses to drive quantum gates is crucial for pushing gate performance toward theoretical limits. In this work, we leverage reinforcement learning (RL) techniques to discover near-optimal pulse shapes that yield PE gates. A collection of RL agents is trained within robust simulation environments, enabling the identification of effective control strategies even under noisy conditions. Selected agents are then validated on higher-fidelity simulations, illustrating how RL-based methods can reduce calibration overhead when compared to quantum optimal control techniques. Furthermore, the RL approach is hardware agnostic with the potential for broad applicability across various quantum computing platforms.
{"title":"Achieving fast and robust perfect entangling gates via reinforcement learning","authors":"Leander Grech, Matthias G Krauss, Mirko Consiglio, Tony J G Apollaro, Christiane P Koch, Simon Hirlaender and Gianluca Valentino","doi":"10.1088/2058-9565/ae2c16","DOIUrl":"https://doi.org/10.1088/2058-9565/ae2c16","url":null,"abstract":"Noisy intermediate-scale quantum computers hold the promise of tackling complex and otherwise intractable computational challenges through the massive parallelism offered by qubits. Central to realizing the potential of quantum computing are perfect entangling (PE) two-qubit gates, which serve as a critical building block for universal quantum computation. In the context of quantum optimal control, shaping electromagnetic pulses to drive quantum gates is crucial for pushing gate performance toward theoretical limits. In this work, we leverage reinforcement learning (RL) techniques to discover near-optimal pulse shapes that yield PE gates. A collection of RL agents is trained within robust simulation environments, enabling the identification of effective control strategies even under noisy conditions. Selected agents are then validated on higher-fidelity simulations, illustrating how RL-based methods can reduce calibration overhead when compared to quantum optimal control techniques. Furthermore, the RL approach is hardware agnostic with the potential for broad applicability across various quantum computing platforms.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"22 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145813024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1088/2058-9565/ae2b31
Alon Levi, Ziv Ossi, Eliahu Cohen and Amit Te’eni
A hybrid quantum–classical algorithm is a computational scheme in which quantum circuits are used to extract information that is then processed by a classical routine to guide subsequent quantum operations. These algorithms are especially valuable in the noisy intermediate-scale quantum era, where quantum resources are constrained and classical optimization plays a central role. Here, we improve the performance of a hybrid algorithm through principled, information-theoretic optimization. We focus on Quantum Likelihood Estimation (QLE)–-a hybrid algorithm designed to identify the Hamiltonian governing a quantum system by iteratively updating a weight distribution based on measurement outcomes and Bayesian inference. While QLE already achieves convergence using quantum measurements and Bayesian inference, its efficiency can vary greatly depending on the choice of parameters at each step. We propose an optimization strategy that dynamically selects the initial state, measurement basis, and evolution time in each iteration to maximize the mutual information between the measurement outcome and the true Hamiltonian. This approach builds upon the information-theoretic framework recently developed in Te’eni et al (2024 arXiv:2409.15549), and leverages mutual information as a guiding cost function for parameter selection. Our implementation employs a simulated annealing routine to minimize the conditional von Neumann entropy, thereby maximizing information gain in each iteration. The results demonstrate that our optimized version significantly reduces the number of iterations required for convergence, thus proposing a practical method for accelerating Hamiltonian learning in quantum systems. Finally, we propose a general scheme that extends our approach to solve a broader family of quantum learning problems.
{"title":"Optimal quantum likelihood estimation","authors":"Alon Levi, Ziv Ossi, Eliahu Cohen and Amit Te’eni","doi":"10.1088/2058-9565/ae2b31","DOIUrl":"https://doi.org/10.1088/2058-9565/ae2b31","url":null,"abstract":"A hybrid quantum–classical algorithm is a computational scheme in which quantum circuits are used to extract information that is then processed by a classical routine to guide subsequent quantum operations. These algorithms are especially valuable in the noisy intermediate-scale quantum era, where quantum resources are constrained and classical optimization plays a central role. Here, we improve the performance of a hybrid algorithm through principled, information-theoretic optimization. We focus on Quantum Likelihood Estimation (QLE)–-a hybrid algorithm designed to identify the Hamiltonian governing a quantum system by iteratively updating a weight distribution based on measurement outcomes and Bayesian inference. While QLE already achieves convergence using quantum measurements and Bayesian inference, its efficiency can vary greatly depending on the choice of parameters at each step. We propose an optimization strategy that dynamically selects the initial state, measurement basis, and evolution time in each iteration to maximize the mutual information between the measurement outcome and the true Hamiltonian. This approach builds upon the information-theoretic framework recently developed in Te’eni et al (2024 arXiv:2409.15549), and leverages mutual information as a guiding cost function for parameter selection. Our implementation employs a simulated annealing routine to minimize the conditional von Neumann entropy, thereby maximizing information gain in each iteration. The results demonstrate that our optimized version significantly reduces the number of iterations required for convergence, thus proposing a practical method for accelerating Hamiltonian learning in quantum systems. Finally, we propose a general scheme that extends our approach to solve a broader family of quantum learning problems.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"56 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145777639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1088/2058-9565/ae2887
Maria-Andreea Filip and Nathan Fitzpatrick
We consider a quantum algorithm for ground-state preparation based on a Chebyshev series approximation to the wall function. In a classical setting, this approach is appealing as it guarantees rapid convergence. We analyse the asymptotic scaling and success probabilities of different quantum implementations and provide numerical benchmarks, comparing the performance of the wall-Chebyshev projectors with current state-of-the-art approaches. We find that this approach requires fewer serial applications of the Hamiltonian oracle to achieve a given ground state fidelity, but is severely limited by exponentially decaying success probability. However, we find that some implementations maintain non-trivial success probability in regimes where wall-Chebyshev projection leads to a fidelity improvement over other approaches. As the wall-Chebyshev projector is highly robust to loose known upper bounds on the true ground state energy, it offers a potential resource trade-off, particularly in the early fault-tolerant regime of quantum computation.
{"title":"Beyond asymptotic reasoning: the practicalities of a quantum ground state projector based on the wall-Chebyshev expansion","authors":"Maria-Andreea Filip and Nathan Fitzpatrick","doi":"10.1088/2058-9565/ae2887","DOIUrl":"https://doi.org/10.1088/2058-9565/ae2887","url":null,"abstract":"We consider a quantum algorithm for ground-state preparation based on a Chebyshev series approximation to the wall function. In a classical setting, this approach is appealing as it guarantees rapid convergence. We analyse the asymptotic scaling and success probabilities of different quantum implementations and provide numerical benchmarks, comparing the performance of the wall-Chebyshev projectors with current state-of-the-art approaches. We find that this approach requires fewer serial applications of the Hamiltonian oracle to achieve a given ground state fidelity, but is severely limited by exponentially decaying success probability. However, we find that some implementations maintain non-trivial success probability in regimes where wall-Chebyshev projection leads to a fidelity improvement over other approaches. As the wall-Chebyshev projector is highly robust to loose known upper bounds on the true ground state energy, it offers a potential resource trade-off, particularly in the early fault-tolerant regime of quantum computation.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"12 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1088/2058-9565/ae2885
Danilo Triggiani and Cosmo Lupo
The Rayleigh diffraction limit imposes a fundamental restriction on the resolution of direct imaging systems, hindering the identification of incoherent optical sources, such as celestial bodies in astronomy and fluorophores in bioimaging. Recent advances in quantum sensing have shown that this limit can be circumvented through spatial demultiplexing (SPADE) and photon detection, i.e. a semi-classical detection strategy. However, the general optimality for arbitrary intensity distributions and bright sources remains unproven. In this work, we develop a general model for incoherent light with arbitrary intensity undergoing diffraction. We employ this framework to compute the quantum Chernoff exponent for generic incoherent-source discrimination problems, focusing on the sub-diffraction regime. We show that, surprisingly, SPADE measurements saturate the quantum Chernoff bound only when certain compatibility conditions are met. These findings suggest that collective measurements may actually be needed to achieve the ultimate quantum Chernoff bound for the discrimination of specific incoherent sources. For the fully general case, our analysis can still be used to find the best SPADE configurations, generally achieved through a rotation of the SPADE interferometer that depends on the discrimination task. We also simulated the efficiency of a simplified Bayesian test that we developed for this identification task and show that the saturation of the Chernoff bound is already achieved for a finite number of repetitions . Our results advance the theory of quantum-limited optical discrimination, with possible applications in diagnostics, automated image interpretation, and galaxy identification.
{"title":"Achieving quantum-limited sub-Rayleigh identification of incoherent optical sources with arbitrary intensities","authors":"Danilo Triggiani and Cosmo Lupo","doi":"10.1088/2058-9565/ae2885","DOIUrl":"https://doi.org/10.1088/2058-9565/ae2885","url":null,"abstract":"The Rayleigh diffraction limit imposes a fundamental restriction on the resolution of direct imaging systems, hindering the identification of incoherent optical sources, such as celestial bodies in astronomy and fluorophores in bioimaging. Recent advances in quantum sensing have shown that this limit can be circumvented through spatial demultiplexing (SPADE) and photon detection, i.e. a semi-classical detection strategy. However, the general optimality for arbitrary intensity distributions and bright sources remains unproven. In this work, we develop a general model for incoherent light with arbitrary intensity undergoing diffraction. We employ this framework to compute the quantum Chernoff exponent for generic incoherent-source discrimination problems, focusing on the sub-diffraction regime. We show that, surprisingly, SPADE measurements saturate the quantum Chernoff bound only when certain compatibility conditions are met. These findings suggest that collective measurements may actually be needed to achieve the ultimate quantum Chernoff bound for the discrimination of specific incoherent sources. For the fully general case, our analysis can still be used to find the best SPADE configurations, generally achieved through a rotation of the SPADE interferometer that depends on the discrimination task. We also simulated the efficiency of a simplified Bayesian test that we developed for this identification task and show that the saturation of the Chernoff bound is already achieved for a finite number of repetitions . Our results advance the theory of quantum-limited optical discrimination, with possible applications in diagnostics, automated image interpretation, and galaxy identification.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"7 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-16DOI: 10.1088/2058-9565/ae2884
Nicolas Dirnegger, Moein Malekakhlagh, Vikesh Siddhu, Ashutosh Rao, Chi Xiong, Muir Kumph, Jason Orcutt and Abram Falk
A promising quantum computing architecture comprises modules of superconducting quantum processors linked via optical channels using quantum transducers. As quantum transducer hardware improves, a need has arisen to understand the quantitative relationship between transducer-device characteristics and the strength of the resulting remote entanglement. Using Monte Carlo simulations that incorporate 2-to-1 and 3-to-1 entanglement distillation methods, our model maps transducer device performance up to system-level channel performance, thereby allowing the performance of remote entanglement approaches to be compared and optimized. We find the extreme photon loss (EPL) distillation protocol to be particularly high performing. Moreover, even without distillation, present-day transducers with added noise of photons are at the threshold of enabling remote Bell pairs with fidelities exceeding 50%. If the next generation of transducers can improve by 3 orders of magnitude in added noise, efficiency, and repetition rates, then they would allow for remote two-qubit gates achieving 99.7% fidelities at MHz rates. These results set practical targets for transducers to be ready for deployment into modular quantum computing systems.
{"title":"Distilled remote entanglement between superconducting qubits across optical channels","authors":"Nicolas Dirnegger, Moein Malekakhlagh, Vikesh Siddhu, Ashutosh Rao, Chi Xiong, Muir Kumph, Jason Orcutt and Abram Falk","doi":"10.1088/2058-9565/ae2884","DOIUrl":"https://doi.org/10.1088/2058-9565/ae2884","url":null,"abstract":"A promising quantum computing architecture comprises modules of superconducting quantum processors linked via optical channels using quantum transducers. As quantum transducer hardware improves, a need has arisen to understand the quantitative relationship between transducer-device characteristics and the strength of the resulting remote entanglement. Using Monte Carlo simulations that incorporate 2-to-1 and 3-to-1 entanglement distillation methods, our model maps transducer device performance up to system-level channel performance, thereby allowing the performance of remote entanglement approaches to be compared and optimized. We find the extreme photon loss (EPL) distillation protocol to be particularly high performing. Moreover, even without distillation, present-day transducers with added noise of photons are at the threshold of enabling remote Bell pairs with fidelities exceeding 50%. If the next generation of transducers can improve by 3 orders of magnitude in added noise, efficiency, and repetition rates, then they would allow for remote two-qubit gates achieving 99.7% fidelities at MHz rates. These results set practical targets for transducers to be ready for deployment into modular quantum computing systems.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"3 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1088/2058-9565/ae23f4
Felix Huber and Jens Siewert
In a seminal article, Higuchi and Sudbery showed that a pure four-qubit state cannot be maximally entangled across every bipartition. Such states are now known as absolutely maximally entangled (AME) states. Here we give a series of old and new proofs of the fact that no four-qubit AME state exists. These are based on invariant theory, methods from coding theory, and basic properties from linear algebra such as the Pauli commutation relations.
{"title":"On two maximally entangled couples","authors":"Felix Huber and Jens Siewert","doi":"10.1088/2058-9565/ae23f4","DOIUrl":"https://doi.org/10.1088/2058-9565/ae23f4","url":null,"abstract":"In a seminal article, Higuchi and Sudbery showed that a pure four-qubit state cannot be maximally entangled across every bipartition. Such states are now known as absolutely maximally entangled (AME) states. Here we give a series of old and new proofs of the fact that no four-qubit AME state exists. These are based on invariant theory, methods from coding theory, and basic properties from linear algebra such as the Pauli commutation relations.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"39 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-12DOI: 10.1088/2058-9565/ae20b8
Rishi Goel, Casey R Myers and Sally Shrapnel
Modern machine learning (ML) methods typically fail to adequately capture causal information. Consequently, such models do not handle data distributional shifts, are vulnerable to adversarial examples, and often learn spurious correlations (Schölkopf and von Kügelgen 2022 (arXiv:2204.00607) [cs.AI]). Causal ML, or causal inference, aims to solve these issues by estimating the expected outcome of counterfactual events, using observational and/or interventional data, where causal relationships are typically depicted as directed acyclic graphs. It is an open question as to whether these causal algorithms provide opportunities for quantum enhancement. In this paper we consider a recently developed family of non-parametric, continuous causal estimators and derive quantum algorithms for these tasks. Kernel evaluation and large matrix inversion are critical sub-routines of these classical algorithms, which makes them particularly amenable to a quantum treatment. Unlike other quantum ML algorithms, closed form solutions for the estimators exist, negating the need for gradient evaluation and variational learning. We describe several new hybrid quantum–classical algorithms and show that uniform consistency of the estimators is retained. Furthermore, if one is satisfied with a quantum state output that is proportional to the true causal estimand, then these algorithms inherit the exponential complexity advantages given by quantum linear system solvers.
{"title":"Quantum algorithms for causal estimands","authors":"Rishi Goel, Casey R Myers and Sally Shrapnel","doi":"10.1088/2058-9565/ae20b8","DOIUrl":"https://doi.org/10.1088/2058-9565/ae20b8","url":null,"abstract":"Modern machine learning (ML) methods typically fail to adequately capture causal information. Consequently, such models do not handle data distributional shifts, are vulnerable to adversarial examples, and often learn spurious correlations (Schölkopf and von Kügelgen 2022 (arXiv:2204.00607) [cs.AI]). Causal ML, or causal inference, aims to solve these issues by estimating the expected outcome of counterfactual events, using observational and/or interventional data, where causal relationships are typically depicted as directed acyclic graphs. It is an open question as to whether these causal algorithms provide opportunities for quantum enhancement. In this paper we consider a recently developed family of non-parametric, continuous causal estimators and derive quantum algorithms for these tasks. Kernel evaluation and large matrix inversion are critical sub-routines of these classical algorithms, which makes them particularly amenable to a quantum treatment. Unlike other quantum ML algorithms, closed form solutions for the estimators exist, negating the need for gradient evaluation and variational learning. We describe several new hybrid quantum–classical algorithms and show that uniform consistency of the estimators is retained. Furthermore, if one is satisfied with a quantum state output that is proportional to the true causal estimand, then these algorithms inherit the exponential complexity advantages given by quantum linear system solvers.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"170 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1088/2058-9565/ae2292
Michelle Wynne Sze, Yao Tang, Silas Dilkes, David Muñoz Ramo, Ross Duncan and Nathan Fitzpatrick
The linear combination of unitaries (LCU) method has proven to scale better than existing product formulas in simulating long time Hamiltonian dynamics. However, given the number of multi-control gate operations in the standard prepare-select-unprepare architecture of LCU, it is still resource-intensive to implement on the current quantum computers. In this work, we demonstrate LCU implementations on an ion trap quantum computer for calculating squared overlaps of time-evolved states. This is achieved by an optimized LCU method, based on pre-selecting relevant unitaries, coupled with a compilation strategy which makes use of quantum multiplexor gates, leading to a significant reduction in the depth and number of two-qubit(2Q) gates in circuits. For L Pauli strings in a Taylor series expanded n-qubit-mapped time evolution operator, we find a 2Q gate count of . We test this approach by simulating a Rabi–Hubbard Hamiltonian.
{"title":"Hamiltonian dynamics simulation using linear combination of unitaries on an ion trap quantum computer","authors":"Michelle Wynne Sze, Yao Tang, Silas Dilkes, David Muñoz Ramo, Ross Duncan and Nathan Fitzpatrick","doi":"10.1088/2058-9565/ae2292","DOIUrl":"https://doi.org/10.1088/2058-9565/ae2292","url":null,"abstract":"The linear combination of unitaries (LCU) method has proven to scale better than existing product formulas in simulating long time Hamiltonian dynamics. However, given the number of multi-control gate operations in the standard prepare-select-unprepare architecture of LCU, it is still resource-intensive to implement on the current quantum computers. In this work, we demonstrate LCU implementations on an ion trap quantum computer for calculating squared overlaps of time-evolved states. This is achieved by an optimized LCU method, based on pre-selecting relevant unitaries, coupled with a compilation strategy which makes use of quantum multiplexor gates, leading to a significant reduction in the depth and number of two-qubit(2Q) gates in circuits. For L Pauli strings in a Taylor series expanded n-qubit-mapped time evolution operator, we find a 2Q gate count of . We test this approach by simulating a Rabi–Hubbard Hamiltonian.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"240 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145711209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1088/2058-9565/ae1d4b
Giovanni Scala, Anindita Bera and Gniewomir Sarbicki
We compute all third-order local invariants accessible via randomized measurements and employ them to derive separability criteria. The reconstruction of the invariants yields experimentally accessible entanglement criteria for multipartite states with arbitrary local dimensions. The results show that third-order invariants capture inter-subsystem correlations beyond second-order spectral criteria within more feasible entanglement detection protocols than full tomography. As an example, for Werner states in d = 3, the entanglement is detected for for the second-order correlations and it is improved to at the third-order.
{"title":"Entanglement detection via third-order local invariants from randomized measurements","authors":"Giovanni Scala, Anindita Bera and Gniewomir Sarbicki","doi":"10.1088/2058-9565/ae1d4b","DOIUrl":"https://doi.org/10.1088/2058-9565/ae1d4b","url":null,"abstract":"We compute all third-order local invariants accessible via randomized measurements and employ them to derive separability criteria. The reconstruction of the invariants yields experimentally accessible entanglement criteria for multipartite states with arbitrary local dimensions. The results show that third-order invariants capture inter-subsystem correlations beyond second-order spectral criteria within more feasible entanglement detection protocols than full tomography. As an example, for Werner states in d = 3, the entanglement is detected for for the second-order correlations and it is improved to at the third-order.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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