npj Quantum Information最新文献

The electrical characterisation of classical and quantum devices is a critical step in the development cycle of heterogeneous material stacks for semiconductor spin qubits. In the case of silicon, properties such as disorder and energy separation of conduction band valleys are commonly investigated individually upon modifications in selected parameters of the material stack. However, this reductionist approach fails to consider the interdependence between different structural and electronic properties at the danger of optimising one metric at the expense of the others. Here, we achieve a significant improvement in both disorder and valley splitting by taking a co-design approach to the material stack. We demonstrate isotopically purified, strained quantum wells with high mobility of 3.14(8) × 10⁵ cm² V⁻¹ s⁻¹ and low percolation density of 6.9(1) × 10¹⁰ cm⁻². These low disorder quantum wells support quantum dots with low charge noise of 0.9(3) μeV Hz^−1/2 and large mean valley splitting energy of 0.24(7) meV, measured in qubit devices. By striking the delicate balance between disorder, charge noise, and valley splitting, these findings provide a benchmark for silicon as a host semiconductor for quantum dot qubits. We foresee the application of these heterostructures in larger, high-performance quantum processors.

We propose a quantum processor architecture, the qubit ‘pipeline’, in which run-time scales additively as functions of circuit depth and run repetitions. Run-time control is applied globally, reducing the complexity of control and interconnect resources. This simplification is achieved by shuttling N-qubit states through a large layered physical array of structures which realise quantum logic gates in stages. Thus, the circuit depth corresponds to the number of layers of structures. Subsequent N-qubit states are ‘pipelined’ densely through the structures to efficiently wield the physical resources for repeated runs. Pipelining thus lends itself to noisy intermediate-scale quantum (NISQ) applications, such as variational quantum eigensolvers, which require numerous repetitions of the same or similar calculations. We illustrate the architecture by describing a realisation in the naturally high-density and scalable silicon spin qubit platform, which includes a universal gate set of sufficient fidelity under realistic assumptions of qubit variability.

We present a direct comparison between QAOA (Quantum Alternating Operator Ansatz), and QA (Quantum Annealing) on 127 qubit problem instances. QAOA with p = 1, 2 rounds is executed on the 127 qubit heavy-hex graph gate-model quantum computer ibm_washington, using on-device grid-searches for angle finding, and QA is executed on two Pegasus-chip D-Wave quantum annealers. The problems are random Ising models whose connectivity matches heavy-hex graphs and the Pegasus graph connectivity, and optionally include hardware-compatible cubic terms (ZZZ terms). The QAOA circuits are heavily optimized and of extremely short depth, with a CNOT depth of 6 per round, which allows whole chip usage of the heavy-hex lattice. QAOA and QA are both compared against simulated annealing and the optimal solutions are computed exactly using CPLEX. The noiseless mean QAOA expectation values for p = 1, 2 are computed using classical light-cone based simulations. We find QA outperforms QAOA on the evaluated devices.

Quantum effects in photosynthetic energy transport in nature, especially for the typical Fenna-Matthews-Olson (FMO) complexes, are extensively studied in quantum biology. Such energy transport processes can be investigated as open quantum systems that blend the quantum coherence and environmental noise, and have been experimentally simulated on a few quantum devices. However, the existing experiments always lack a solid quantum simulation for the FMO energy transport due to their constraints to map a variety of issues in actual FMO complexes that have rich biological meanings. Here we successfully map the full coupling profile of the seven-site FMO structure by comprehensive characterisation and precise control of the evanescent coupling of the three-dimensional waveguide array. By applying a stochastic dynamical modulation on each waveguide, we introduce the base site energy and the dephasing term in coloured noise to faithfully simulate the power spectral density of the FMO complexes. We show our photonic model well interprets the phenomena including reorganisation energy, vibrational assistance, exciton transfer and energy localisation. We further experimentally demonstrate the existence of an optimal transport efficiency at certain dephasing strength, providing a window to closely investigate environment-assisted quantum transport.

Spin qubits in gate-defined silicon quantum dots are receiving increased attention thanks to their potential for large-scale quantum computing. Readout of such spin qubits is done most accurately and scalably via Pauli spin blockade (PSB), however, various mechanisms may lift PSB and complicate readout. In this work, we present an experimental study of PSB in a multi-electron low-symmetry double quantum dot (DQD) in silicon nanowires. We report on the observation of non-symmetric PSB, manifesting as blockaded tunneling when the spin is projected to one QD of the pair but as allowed tunneling when the projection is done into the other. By analyzing the interaction of the DQD with a readout resonator, we find that PSB lifting is caused by a large coupling between the different electron spin manifolds of 7.90 μeV and that tunneling is incoherent. Further, magnetospectroscopy of the DQD in 16 charge configurations, enables reconstructing the energy spectrum of the DQD and reveals the lifting mechanism is energy-level selective. Our results indicate enhanced spin-orbit coupling which may enable all-electrical qubit control of electron spins in silicon nanowires.

The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its optimization hardness. In this work, we investigate such landscapes through the lens of information content, a measure of the variability between points in parameter space. Our major contribution connects the information content to the average norm of the gradient, for which we provide robust analytical bounds on its estimators. This result holds for any (classical or quantum) variational landscape. We validate the analytical understating by numerically studying the scaling of the gradient in an instance of the barren plateau problem. In such instance, we are able to estimate the scaling pre-factors in the gradient. Our work provides a way to analyze variational quantum algorithms in a data-driven fashion well-suited for near-term quantum computers.

Variational quantum metrology represents a powerful tool to optimize estimation strategies, resulting particularly beneficial for multiparameter estimation problems that often suffer from limitations due to the curse of dimensionality and computational complexity. To overcome these challenges, we develop a variational approach able to efficiently optimize a quantum multiphase sensor. Leveraging the reconfigurability of an integrated photonic device, we implement a hybrid quantum-classical feedback loop able to enhance the estimation performances. The quantum circuit evaluations are used to compute the system partial derivatives by applying the parameter-shift rule, and thus reconstruct experimentally the Fisher information matrix. This in turn is adopted as the cost function of a classical learning algorithm run to optimize the measurement settings. Our experimental results showcase significant improvements in estimation accuracy and noise robustness, highlighting the potential of variational techniques for practical applications in quantum sensing and more generally in quantum information processing using photonic circuits.

Continuous-variable (CV) quantum information processing harnesses versatile experimental tools that leverage the power of infinite-dimensional oscillators controlled by a single qubit. Increasingly available elementary Rabi gates have been proposed as a resource for implementing universal CV gates, but the requirement of many weak, non-commuting gates is a bottleneck in scaling up such an approach. In this study, we propose a resource-efficient technique using Fourier expansion to implement arbitrary non-linear phase gates in a single oscillator. This method reduces the number of sequentially required gates exponentially. These gates represented by cubic, quartic, and other arbitrary nonlinear potentials have applications in CV quantum information processing with infinite-dimensional oscillators controlled by a single qubit. Our method outperforms previous approaches and enables the experimental realization of a wide range of applications, including the development of bosonic quantum sensors, simulations, and computation using trapped ions and superconducting circuits.

Wide-field magnetic imaging based on nitrogen-vacancy (NV) centers in diamond has been shown the applicability in material and biological science. However, the spatial resolution is limited by the optical diffraction limit (>200 nm) due to the optical real-space localization and readout of NV centers. Here, we report the wide-field Fourier magnetic imaging technique to improve spatial resolution beyond the optical diffraction limit while maintaining the large field of view. Our method relies on wide-field pulsed magnetic field gradient encoding of NV spins and Fourier transform under pixel-dependent spatial filters. We have improved spatial resolution by a factor of 20 compared to the optical resolution and demonstrated the wide-field super-resolution magnetic imaging of a gradient magnetic field. This technique paves a way for efficient magnetic imaging of large-scale fine structures at the nanoscale.

We report better-than-classical success probabilities for a complete Grover quantum search algorithm on the largest scale demonstrated to date, of up to five qubits, using two different IBM platforms. This is enabled by error suppression via robust dynamical decoupling. Further improvements arise after the use of measurement error mitigation, but the latter is insufficient by itself for achieving better-than-classical performance. For two qubits, we demonstrate a 99.5% success probability via the use of the [[4, 2, 2]] quantum error-detection (QED) code. This constitutes a demonstration of quantum algorithmic breakeven via QED. Along the way, we introduce algorithmic error tomography (AET), a method that provides a holistic view of the errors accumulated throughout an entire quantum algorithm, filtered via the errors detected by the QED code used to encode the circuit. We demonstrate that AET provides a stringent test of an error model based on a combination of amplitude damping, dephasing, and depolarization.