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arXiv:2604.00212

Building Block For Universal Continuous Variables Computation In Superconducting Devices

Bruno A. Veloso, Ciro M. Diniz, Luiz O. R. Solak, Antonio S. M. de Castro, Daniel Z. Rossatto, Celso J. Villas-Bôas

quant-phSubmitted Mar 31, 2026• Announced Apr 2, 2026• 0 citations• Revised Apr 2, 2026 (v2)
v1 · Mar 31, 2026v2 · Apr 2, 2026
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Abstract

Continuous variable (CV) quantum computation offers an alternative to qubit-based computing by exploiting the infinite-dimensional Hilbert space of bosonic modes. Despite recent progress, superconducting platforms have yet to demonstrate a scalable architecture capable of universal computation. Here, we design and numerically simulate a two-layer superconducting architecture that implements all five interactions of the universal CV gate set (rotation, displacement, squeezing, Kerr, and beam splitter) within experimentally accessible regimes. To this end, we employ a DC-SQUID as the bosonic mode, a fluxonium qubit to mediate nonlinear interactions, and two ancillary qubits that enable Gaussian and multi-mode operations. By tuning fluxes and frequencies, we achieve high fidelities ($\geq 98\%$) across all gates within state-of-the-art parameter ranges. The modular nature of the design allows straightforward scaling, establishing a feasible pathway toward high-fidelity, universal CV quantum computation based on superconducting circuits.

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Continuous variable (CV) quantum computation offers an alternative to qubit-based computing by exploiting the infinite-dimensional Hilbert space of bosonic modes. Despite recent progress, superconducting platforms have yet to demonstrate a scalable architecture capable of universal computation. Here, we design and numerically simulate a two-layer superconducting architecture that implements all five interactions of the universal CV gate set (rotation, displacement, squeezing, Kerr, and beam splitter) within experimentally accessible regimes. To this end, we employ a DC-SQUID as the bosonic mode, a fluxonium qubit to mediate nonlinear interactions, and two ancillary qubits that enable Gaussian and multi-mode operations. By tuning fluxes and frequencies, we achieve high fidelities (≥98%\geq 98\%≥98%) across all gates within state-of-the-art parameter ranges. The modular nature of the design allows straightforward scaling, establishing a feasible pathway toward high-fidelity, universal CV quantum computation based on superconducting circuits.
Abstract Continuous variable (CV) quantum computation offers an alternative to qubit-based computing by exploiting the infinite-dimensional Hilbert space of bosonic modes. Despite recent progress, superconducting platforms have yet to demonstrate a scalable architecture capable of universal computation. Here, we design and numerically simulate a two-layer superconducting architecture that implements all five interactions of the universal CV gate set (rotation, displacement, squeezing, Kerr, and beam splitter) within experimentally accessible regimes. To this end, we employ a DC-SQUID as the bosonic mode, a fluxonium qubit to mediate nonlinear interactions, and two ancillary qubits that enable Gaussian and multi-mode operations. By tuning fluxes and frequencies, we achieve high fidelities ( ⩾ 98 % ) across all gates within state-of-the-art parameter ranges. The modular nature of the design allows straightforward scaling, establishing a feasible pathway toward high-fidelity, universal CV quantum computation based on superconducting circuits.
Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes while non-Gaussian resources (the challenging piece for physical implementation) originate the nonlinear effects, unlocking quantum analogs of an artificial neuron. This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks (QONNs) developed with available photonic components: coherent states as input encoding, a general Gaussian transformation followed by multi-mode photon subtractions as the processing layer, and homodyne detection as outputs readout. The closed-form expressions of such architecture are derived demonstrating the family of adaptive activations and the quantum-optical neurons that emerge from the amount of photon-subtracted modes, proving that the proposed design satisfies the Universal Approximation Theorem within a single layer. To classically simulate the QONN training, the high-performance QuaNNTO library has been developed based on Wick-Isserlis expansion and Bogoliubov transformations, allowing multi-layer exact expectation values of non-Gaussian states without truncating the infinite-dimensional Hilbert space. Experiments on supervised learning and state-preparation tasks show balanced-resource efficiency with strong expressivity and generalization capabilities, illustrating the potential of the architecture for scalable photonic quantum machine learning and for quantum applications such as complex non-Gaussian gate synthesis.
In this article, we report, to the best of our knowledge, for the first time, the design and simulation of half-adder and full-adder circuits implemented using continuous variable (CV) quantum gates within the framework of quantum photonics. Our approach supports the high-dimensional nature of CV and offers an alternative to the commonly used discrete variable (DV) systems for scalable and noise-resilient quantum computing architectures. We utilize Gaussian operations, displacement gates, and beamsplitter-based interferometric circuits to perform quantum arithmetic operations, utilizing CV systems' entanglement and superposition principles. Carefully engineered gate sequences and phase-space manipulations allow us to realize logic operations corresponding to classical addition, with each optical mode representing a computational basis state. We present two distinct circuit designs, one for a half adder and one for a full adder, each verified using a CV simulation environment. Our results highlight the potential of continuous platforms to implement arithmetic logic for hybrid classical-quantum processors. This work opens a new pathway towards optical quantum computing with compact, noise-tolerant logic modules using CV quantum information processing.
We provide an explicit construction of a universal gate set for continuous-variable quantum computation with microwave circuits. Such a universal set has been first proposed in quantum-optical setups, but its experimental implementation has remained elusive in that domain due to the difficulties in engineering strong nonlinearities. Here, we show that a realistic three-wave mixing microwave architecture based on the superconducting nonlinear asymmetric inductive element [Frattini et al., Appl. Phys. Lett. 110, 222603 (2017)APPLAB0003-695110.1063/1.4984142] allows us to overcome this difficulty. As an application, we show that this architecture allows for the generation of a cubic phase state with an experimentally feasible procedure. This work highlights a practical advantage of microwave circuits with respect to optical systems for the purpose of engineering non-Gaussian states and opens the quest for continuous-variable algorithms based on few repetitions of elementary gates from the continuous-variable universal set.
Multimode superconducting parametric cavity is a flexible platform that has been used to study a variety of topics in microwave quantum optics ranging from parametric amplification, entanglement generation to higher order spontaneous parametric downconversion (SPDC). \nLeveraging the extensive toolbox of interactions available in this system, we can look to explore exciting applications in quantum computation and simulation. \nIn this thesis, we study the use of the parametric cavity to realize continuous variable (CV) quantum computation. \nWe propose and examine in detail the scheme to compute with the microwave photons in the orthogonal frequency modes of the cavity via successive application of parametric pump pulses or cavity drives. \nThe family of all Gaussian transformations can be accomplished easily with interactions already demonstrated in this system. \nFrom recent results and proposals involving higher order SPDC, there are also clear pathways towards realizing the non-Gaussian resources necessary for universal computation. \nCommon measurements on the system are accomplished with standard measurement techniques on the output state of the cavity and additional useful measurements may be implemented using available parametric interactions or new device designs involving a qubit as a nonlinear probe. \nUsing the parametric cavity, we experimentally implemented a hybrid quantum-classical machine learning algorithm called the Quantum Kitchen Sinks (QKS) as the first step towards developing this platform for quantum computation. \nThe algorithm is studied over two sets of experiments starting from partial experimental implementation of the quantum variational circuits to fully experimental implementation using multiple simultaneous continuous wave (CW) pumps. \nIn both cases, we find that the quantum part of the algorithm implemented in the parametric cavity improved the classification accuracy on a difficult synthetic data set up to 90.1% and 99.5% respectively when compared to a classical linear machine learning algorithm.
Continuous-variable (CV) quantum light sources are the essential resource for quantum computation. Integrated CV quantum light sources offer a scalable pathway by harnessing low-loss nonlinear materials, versatile device architectures, and CMOS-compatible fabrication processes enabled by integrated photonics platforms. In this review, we briefly introduce recent progress on integrated CV quantum light sources, including single-mode squeezed states, two-mode squeezed states, and multimode entangled states. The key performance metrics of CV quantum light sources, such as the squeezing level, bandwidth, purity, and mode multiplexing, are analyzed. We highlight representative implementations of lithium niobate, silicon nitride, and silica platforms for CV quantum light sources and discuss major challenges for realizing integrated large-scale and fault-tolerant CV quantum computation.