Beyond Hardware: Adaptive Algorithmic Control by State-Proxy Equalization

• URL: http://arxiv.org/abs/2609.17497v1
• Date: Tue, 15 Sep 2026 17:37:39 GMT
• Title: Beyond Hardware: Adaptive Algorithmic Control by State-Proxy Equalization
• Abstract summary: We show that substantial gains can arise from how finite computational resources are allocated throughout a quantum computation.<n>Across quantum optimization problems containing up to 156 qubits, A2C improves the low-energy sampling probabilities by $22%$ to over $100,000%$ under matched circuit depths and measurement budgets.
• Score: 4.748542401878416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in quantum computing have been driven primarily by improvements in hardware. Here we show that substantial gains can instead arise from how finite computational resources are allocated throughout a quantum computation. We introduce Adaptive Algorithmic Control (A2C), a software paradigm founded on a State-Proxy Equalization theorem, which proves that the optimal allocation for a state-derived proxy-error functional equalizes cumulative computational hardness rather than physical time. The required computational hardness is inferred directly from the evolving quantum state, avoiding explicit reconstruction of the exponentially large many-body spectrum. Across quantum optimization problems containing up to 156 qubits, combining exact simulations, large-scale supercomputer computations and IBM quantum hardware experiments, A2C improves the low-energy sampling probabilities by $22\%$ to over $100,000\%$ under matched circuit depths and measurement budgets. These results demonstrate that quantum computational performance depends not only on hardware capabilities, but also on how finite computational resources are organized, establishing adaptive algorithmic control as a complementary software pathway for advancing quantum computation.

Related papers

• Adaptive Quantum Computers: decoding and state preparation [0.0]
  This work formalizes a model that describes adaptive quantum computers.<n>We show that adaptive quantum computers are more powerful than standard computers with respect to retrieving information from corrupted digital data.<n>Next, we show how adaptive quantum computations can improve non-adaptive quantum computations when preparing specific quantum states.
  arXiv  Detail & Related papers  (2025-09-10T16:13:41Z)
• 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)
• Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects [59.07692103357675]
  This survey explores the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware.<n>It becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
  arXiv  Detail & Related papers  (2024-06-30T15:50:10Z)
• Scalable Quantum Algorithms for Noisy Quantum Computers [0.0]
  This thesis develops two main techniques to reduce the quantum computational resource requirements. The aim is to scale up application sizes on current quantum processors. While the main focus of application for our algorithms is the simulation of quantum systems, the developed subroutines can further be utilized in the fields of optimization or machine learning.
  arXiv  Detail & Related papers  (2024-03-01T19:36:35Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Towards Quantum Computational Mechanics [1.530480694206666]
  We show how quantum computing can be used to solve representative element volume (RVE) problems in computational homogenisation. Our quantum RVE solver attains exponential acceleration with respect to classical solvers.
  arXiv  Detail & Related papers  (2023-12-06T12:53:02Z)
• 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)
• Schrödinger as a Quantum Programmer: Estimating Entanglement via Steering [3.187381965457262]
  We develop a quantum algorithm that tests for and quantifies the separability of a general bipartite state by using the quantum steering effect. Our findings provide a meaningful connection between steering, entanglement, quantum algorithms, and quantum computational complexity theory.
  arXiv  Detail & Related papers  (2023-03-14T13:55:06Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation [50.591267188664666]
  Quantum chemistry and materials is one of the most promising applications of quantum computing. Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
  arXiv  Detail & Related papers  (2022-03-14T16:51:36Z)
• Long-Time Error-Mitigating Simulation of Open Quantum Systems on Near Term Quantum Computers [38.860468003121404]
  We study an open quantum system simulation on quantum hardware, which demonstrates robustness to hardware errors even with deep circuits containing up to two thousand entangling gates. We simulate two systems of electrons coupled to an infinite thermal bath: 1) a system of dissipative free electrons in a driving electric field; and 2) the thermalization of two interacting electrons in a single orbital in a magnetic field -- the Hubbard atom. Our results demonstrate that algorithms for simulating open quantum systems are able to far outperform similarly complex non-dissipative algorithms on noisy hardware.
  arXiv  Detail & Related papers  (2021-08-02T21:36:37Z)
• As Accurate as Needed, as Efficient as Possible: Approximations in DD-based Quantum Circuit Simulation [5.119310422637946]
  Decision Diagrams (DDs) have previously shown to reduce the required memory in many important cases by exploiting redundancies in the quantum state. We show that this reduction can be amplified by exploiting the probabilistic nature of quantum computers to achieve even more compact representations. Specifically, we propose two new DD-based simulation strategies that approximate the quantum states to attain more compact representations.
  arXiv  Detail & Related papers  (2020-12-10T12:02:03Z)

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.