Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data

• URL: http://arxiv.org/abs/2312.16165v2
• Date: Fri, 30 Aug 2024 16:08:26 GMT
• Title: Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data
• Authors: Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci,
• Abstract summary: We present a machine learning algorithm, NISQRC, for qubit-based quantum systems. We show that NISQRC can recover arbitrarily long test signals, not limited by coherence time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time.

Related papers

• Reinforcement Learning Control of Quantum Error Correction [108.70420561323692]
  Quantum computer learns to self-improve directly from its errors and never stops computing.<n>This work enables a new paradigm: a quantum computer that learns to self-improve directly from its errors and never stops computing.
  arXiv  Detail & Related papers  (2025-11-11T17:32:25Z)
• Extending coherence time beyond break-even point using only drives and dissipation [12.35444043278315]
  Quantum error correction (QEC) aims to mitigate the loss of quantum information to the environment.<n>We propose an AQEC protocol, derived from quantum channel simulation, that is applicable to arbitrary error-correcting codes.<n>This is the first experimental realization of an AQEC-protected bosonic logical qubit beyond the break-even point.
  arXiv  Detail & Related papers  (2025-09-26T10:50:27Z)
• Optimal training of finitely-sampled quantum reservoir computers for forecasting of chaotic dynamics [3.7960472831772765]
  In the current Noisy Intermediate Scale Quantum (NISQ) era, the presence of noise deteriorates the performance of quantum computing algorithms. In this paper, we analyse the effect that finite-sampling noise has on the chaotic time-series prediction capabilities of Quantum Reservoir Computing (QRC) and Recurrence-free Quantum Reservoir Computing (RF-QRC) We show that finite sampling noise degrades the prediction capabilities of both QRC and RF-QRC while affecting QRC more due to the propagation of noise.
  arXiv  Detail & Related papers  (2024-09-02T17:51:48Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Feedback-driven quantum reservoir computing for time-series analysis [1.1920094285063136]
  Quantum reservoir computing (QRC) is a promising computational paradigm that leverages quantum systems as a computational resource for nonlinear information processing. We propose a feedback-driven QRC framework for time-series analysis. We demonstrate that our QRC successfully acquires the fading-memory property through the feedback connections.
  arXiv  Detail & Related papers  (2024-06-22T08:28:08Z)
• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Measurement-induced entanglement and teleportation on a noisy quantum processor [105.44548669906976]
  We investigate measurement-induced quantum information phases on up to 70 superconducting qubits. We use a duality mapping, to avoid mid-circuit measurement and access different manifestations of the underlying phases. Our work demonstrates an approach to realize measurement-induced physics at scales that are at the limits of current NISQ processors.
  arXiv  Detail & Related papers  (2023-03-08T18:41:53Z)
• Demonstration of algorithmic quantum speedup [0.0]
  An experimental demonstration of a provable algorithmic quantum speedup has remained elusive. We implement the single-shot Bernstein-Vazirani algorithm, which solves the problem of identifying a hidden bitstring. The speedup is observed on only one of the two QCs.
  arXiv  Detail & Related papers  (2022-07-15T17:59:47Z)
• Measurement based estimator scheme for continuous quantum error correction [52.77024349608834]
  Canonical discrete quantum error correction (DQEC) schemes use projective von Neumann measurements on stabilizers to discretize the error syndromes into a finite set. Quantum error correction (QEC) based on continuous measurement, known as continuous quantum error correction (CQEC), can be executed faster than DQEC and can also be resource efficient. We show that by constructing a measurement-based estimator (MBE) of the logical qubit to be protected, it is possible to accurately track the errors occurring on the physical qubits in real time.
  arXiv  Detail & Related papers  (2022-03-25T09:07:18Z)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)
• Reducing runtime and error in VQE using deeper and noisier quantum circuits [0.0]
  A core of many quantum algorithms including VQE, can be improved in terms of precision and accuracy by using a technique we call Robust Amplitude Estimation. By using deeper, and therefore more error-prone, quantum circuits, we realize more accurate quantum computations in less time. This technique may be used to speed up quantum computations into the regime of early fault-tolerant quantum computation.
  arXiv  Detail & Related papers  (2021-10-20T17:11:29Z)
• Experimentally Realizing Efficient Quantum Control with Reinforcement Learning [2.733342606024131]
  We experimentally demonstrate an alternative approach to quantum control based on deep reinforcement learning (DRL) on a trapped $171mathrmYb+$ ion. In particular, we find that DRL leads to fast and robust digital quantum operations with running time bounded by shortcuts to adiabaticity (STA) Our experiments reveal a general framework of digital quantum control, leading to a promising enhancement in quantum information processing.
  arXiv  Detail & Related papers  (2021-01-22T09:34:58Z)
• Boundaries of quantum supremacy via random circuit sampling [69.16452769334367]
  Google's recent quantum supremacy experiment heralded a transition point where quantum computing performed a computational task, random circuit sampling. We examine the constraints of the observed quantum runtime advantage in a larger number of qubits and gates.
  arXiv  Detail & Related papers  (2020-05-05T20:11:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.