A Variational Dissipative Framework for Quantum Algorithms

• URL: http://arxiv.org/abs/2605.25841v1
• Date: Mon, 25 May 2026 13:35:46 GMT
• Title: A Variational Dissipative Framework for Quantum Algorithms
• Abstract summary: We introduce a variational dissipative framework in which engineered dissipation is incorporated into parameterized quantum algorithms.<n>In this framework, system-only variational layers are combined with trainable dissipative modules.<n>We show that engineered dissipation can be incorporated into variational quantum circuits as a reusable trainable primitive.
• Score: 12.862867915765774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dissipation engineering has attracted growing interest as an approach to controlling open quantum systems through engineered system-environment interactions. Standard variational quantum circuits are usually built from unitary operations and therefore explore only a restricted family of states. To go beyond this limitation, we introduce a variational dissipative framework in which ancilla-assisted engineered dissipation is incorporated into parameterized quantum algorithms. In this framework, system-only variational layers are combined with trainable dissipative modules, so that the circuit can prepare a broader class of mixed states through ancilla-assisted nonunitary transformations. Within this framework, the same ancilla-assisted dissipative block is used in two representative settings with different objectives. For ground-state search, it is integrated into a dissipative variational quantum eigensolver to improve the convergence toward low-energy states. For state recovery, it is trained as a recovery channel to suppress preparation noise and enhance fidelity with the target state. In both cases, the block is realized through parameterized system-ancilla couplings followed by ancilla reset and trace-out. Our results show that engineered dissipation can be incorporated into variational quantum circuits as a reusable trainable primitive rather than treated only as a source of noise. In this sense, the proposed framework identifies ancilla-assisted dissipative channels as a concrete variational resource that can support both optimization and recovery tasks within a unified design.

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