Codesigned counterdiabatic quantum optimization on a photonic quantum processor

• URL: http://arxiv.org/abs/2409.17930v1
• Date: Thu, 26 Sep 2024 15:08:19 GMT
• Title: Codesigned counterdiabatic quantum optimization on a photonic quantum processor
• Authors: Xiao-Wen Shang, Xuan Chen, Narendra N. Hegade, Ze-Feng Lan, Xuan-Kun Li, Hao Tang, Yu-Quan Peng, Enrique Solano, Xian-Min Jin,
• Abstract summary: We focus on the counterdiabatic protocol with a codesigned approach to implement this algorithm on a photonic quantum processor. We develop and implement an optimized counterdiabatic method by tackling the higher-order many-body interaction terms. We experimentally demonstrate the advantages of a codesigned mapping of counterdiabatic quantum dynamics for quantum computing on photonic platforms.
• Score: 6.079051215256144
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Codesign, an integral part of computer architecture referring to the information interaction in hardware-software stack, is able to boost the algorithm mapping and execution in the computer hardware. This well applies to the noisy intermediate-scale quantum era, where quantum algorithms and quantum processors both need to be shaped to allow for advantages in experimental implementations. The state-of-the-art quantum adiabatic optimization algorithm faces challenges for scaling up, where the deteriorating optimization performance is not necessarily alleviated by increasing the circuit depth given the noise in the hardware. The counterdiabatic term can be introduced to accelerate the convergence, but decomposing the unitary operator corresponding to the counterdiabatic terms into one and two-qubit gates may add additional burden to the digital circuit depth. In this work, we focus on the counterdiabatic protocol with a codesigned approach to implement this algorithm on a photonic quantum processor. The tunable Mach-Zehnder interferometer mesh provides rich programmable parameters for local and global manipulation, making it able to perform arbitrary unitary evolutions. Accordingly, we directly implement the unitary operation associated to the counterdiabatic quantum optimization on our processor without prior digitization. Furthermore, we develop and implement an optimized counterdiabatic method by tackling the higher-order many-body interaction terms. Moreover, we benchmark the performance in the case of factorization, by comparing the final success probability and the convergence speed. In conclusion, we experimentally demonstrate the advantages of a codesigned mapping of counterdiabatic quantum dynamics for quantum computing on photonic platforms.

Related papers

• Pulse-based variational quantum optimization and metalearning in superconducting circuits [3.770494165043573]
  We introduce pulse-based variational quantum optimization (PBVQO) as a hardware-level framework. We illustrate the framework by optimizing external superconducting on quantum interference devices. The synergy between PBVQO and meta-learning provides an advantage over conventional gate-based variational algorithms.
  arXiv  Detail & Related papers  (2024-07-17T15:05:36Z)
• 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)
• Performant near-term quantum combinatorial optimization [1.1999555634662633]
  We present a variational quantum algorithm for solving optimization problems with linear-depth circuits. Our algorithm uses an ansatz composed of Hamiltonian generators designed to control each term in the target quantum function. We conclude our performant and resource-minimal approach is a promising candidate for potential quantum computational advantages.
  arXiv  Detail & Related papers  (2024-04-24T18:49:07Z)
• Surrogate optimization of variational quantum circuits [1.0546736060336612]
  Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE.
  arXiv  Detail & Related papers  (2024-04-03T18:00:00Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Optimizing quantum gates towards the scale of logical qubits [78.55133994211627]
  A foundational assumption of quantum gates theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Here we report on a strategy that can overcome such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunablebits to execute single qubit while superconducting errors.
  arXiv  Detail & Related papers  (2023-08-04T13:39:46Z)
• Photonic counterdiabatic quantum optimization algorithm [3.2174634059872154]
  We propose a hybrid quantum- approximate optimization algorithm for quantum computing that is tailored for continuous-variable problems. We conduct proof-of-principle experiments on an-photo quantum chip.
  arXiv  Detail & Related papers  (2023-07-27T13:33:33Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)
• Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization [0.14755786263360526]
  We explore which quantum algorithms for optimization might be most practical to try out on a small fault-tolerant quantum computer. Our results discourage the notion that any quantum optimization realizing only a quadratic speedup will achieve an advantage over classical algorithms.
  arXiv  Detail & Related papers  (2020-07-14T22:54:04Z)

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.