Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2508.21265

SCE-NTT: A Hardware Accelerator for Number Theoretic Transform Using Superconductor Electronics

Sasan Razmkhah, Mingye Li, Zeming Cheng, Robert S. Aviles, Kyle Jackman, Joey Delport, Lieze Schindler, Wenhui Luo, T. Suzuki, Mehdi Kamal, Christopher Lawrence Ayala, Coenrad Johann Fourie, Yoshikawa, Nabuyuki, Peter A. Beerel, Sandeep Gupta, Massoud Pedram

Published Aug 28, 2025• 0 citations
arXivHTMLPDF

Abstract

This research explores the use of superconductor electronics (SCE) for accelerating fully homomorphic encryption (FHE), focusing on the Number-Theoretic Transform (NTT), a key computational bottleneck in FHE schemes. We present SCE-NTT, a dedicated hardware accelerator based on superconductive single flux quantum (SFQ) logic and memory, targeting high performance and energy efficiency beyond the limits of conventional CMOS. To address SFQ constraints such as limited dense RAM and restricted fanin/fanout, we propose a deeply pipelined NTT-128 architecture using shift register memory (SRM). Designed for N=128 32-bit coefficients, NTT-128 comprises log2(N)=7 processing elements (PEs), each featuring a butterfly unit (BU), dual coefficient memories operating in ping-pong mode via FIFO-based SRM queues, and twiddle factor buffers. The BU integrates a Shoup modular multiplier optimized for a small area, leveraging precomputed twiddle factors. A new RSFQ cell library with over 50 parameterized cells, including compound logic units, was developed for implementation. Functional and timing correctness were validated using JoSIM analog simulations and Verilog models. A multiphase clocking scheme was employed to enhance robustness and reduce path-balancing overhead, improving circuit reliability. Fabricated results show the NTT-128 unit achieves 531 million NTT/sec at 34 GHz, over 100x faster than state-of-the-art CMOS equivalents. We also project that the architecture can scale to larger sizes, such as a 2^14-point NTT in approximately 482 ns. Key-switch throughput is estimated at 1.63 million operations/sec, significantly exceeding existing hardware. These results demonstrate the strong potential of SCE-based accelerators for scalable, energy-efficient secure computation in the post-quantum era, with further gains anticipated through advances in fabrication.

DOI
10.48550/arxiv.2508.21265

Related papers

Design and Logic Synthesis of a Scalable, Efficient Quantum Number Theoretic Transform

Chao Lu, Shamik Kundu, Abraham Peedikayil Kuruvila +2 more

The advent of quantum computing has engendered a widespread proliferation of efforts utilizing qubits for optimizing classical computational algorithms. Number Theoretic Transform (NTT) is one such popular algorithm that accelerates polynomial multiplication significantly and is consequently, the core arithmetic operation in most homomorphic encryption algorithms. Hence, fast and efficient execution of NTT is highly imperative for practical implementation of homomorphic encryption schemes in different computing paradigms. In this paper, we, for the first time, propose an efficient and scalable Quantum Number Theoretic Transform (QNTT) circuit using quantum gates. We introduce a novel exponential unit for modular exponential operation, which furnishes an algorithmic complexity of O(n). Our proposed methodology performs further optimization and logic synthesis of QNTT, that is significantly fast and facilitates efficient implementations on IBM’s quantum computers. The optimized QNTT achieves a gate-level complexity reduction from power of two to one with respect to bit length. Our methodology utilizes 44.2% fewer gates, thereby minimizing the circuit depth and a corresponding reduction in overhead and error probability, for a 4-point QNTT compared to its unoptimized counterpart.

International Symposium on Low Power Electronics and Design (ISLPED)•Jul 16, 2022•6 citations

Inductorless fast phase logic: Enabling two-order-of-magnitude density scaling for superconductor VLSI

Sasan Razmkhah, Massoud Pedram, Douglas Holmes

Abstract Fast phase logic (FPL) is a novel digital superconductor electronic (SCE) logic family that employs multiple junction types, including switching 0-Josephson junctions (0-JJs), non-switching 0-JJ stacks, and π-JJs. FPL enables flexible, automatable cell layouts, faster pulse propagation, reduced bias current via phaseshifting π-JJs, and minimized inductive loops, thereby reducing susceptibility to trapped flux and crosstalk.A fabrication process to support FPL is proposed. NbTiN superconductors offer small grain sizes, smooth surfaces, and thermal stability up to 400 ○ C, while high-Jc, self-shunted JJs enable compact devices. AlN dielectrics provide good crystal matching to NbTiN, improving superconducting properties. Projections indicate that FPL, combined with the proposed process, can achieve a two-order-of-magnitude increase in integration density over conventional RSFQ logic and a five-fold reduction in supply current. The increased density reduces latency and improves computational throughput, while NbTiN-based devices provide higher output voltage and impedance, improving compatibility with CMOS circuits. Further fabrication advancements, such as higher-Jc NbTiN-based JJs, higher processing temperatures, and stacked JJ structures, could enhance FPL implementation and scalability toward very large-scale integration (VLSI). FPL has the potential to significantly advance SCE technology, with near-term applications in accelerator cores for signal processing and artificial intelligence, and long-term potential in supercomputing. Its advantages are evaluated through an architectural study of a fast Fourier transform (FFT) circuit, with comparisons to CMOS and SFQ technologies for digital electronics.

arXiv:2601.04363•Superconductor Science and Technology•Oct 27, 2026•0 citations

Superconducting Digital DIT Butterfly Unit for Fast Fourier Transform Using Race Logic

Meriam Gay Bautista, Patricia Gonzalez-Guerrero, Darren Lyles +2 more

In this paper we present an FFT hardware accelerator based on a butterfly (BUT) structure implemented in Rapid Single-Flux-Quantum (RSFQ) superconducting computing. Our design uses a data representation that combines Race Logic (RL) with pulse streams in order to perform arithmetic operations and generate twiddle factors efficiently. Given today’s stringent area limitations for RSFQ technology, our design aims to maximize area efficiency. Our design shows 2 to 18× higher throughput per area, measured as performance per number of Josephson junctions. Moreover, we demonstrate 20-70% area savings compared to binary RSFQ FFT implementations for 2 to 64 N-points.

IEEE Interregional NEWCAS Conference (NEWCAS)•Jun 19, 2022•4 citations

Implementation of an 8-bit bit-slice AES S-box with rapid single flux quantum circuits

Ruoting Yang, Xin-Yi Xue, Shucheng Yang +4 more

Rapid single flux quantum (RSFQ) circuits are a kind of superconducting digital circuits, having properties of a natural gate-level pipelining synchronous sequential circuit, which demonstrates high energy efficiency and high throughput advantage. We find that the high-throughput and high-speed performance of RSFQ circuits can take the advantage of a hardware implementation of the encryption algorithm, whereas these are rarely applied to this field. Among the available encryption algorithms, the advanced encryption standard (AES) algorithm is an advanced encryption standard algorithm. It is currently the most widely used symmetric cryptography algorithm. In this work, we aim to demonstrate the SubByte operation of an AES-128 algorithm using RSFQ circuits based on the SIMIT Nb03 process. We design an AES S-box circuit in the RSFQ logic, and compare its operational frequency, power dissipation, and throughput with those of the CMOS-based circuit post-simulated in the same structure. The complete RSFQ S-box circuit costs a total of 42237 Josephson junctions with nearly 130 Gbps throughput under the maximum simulated frequency of 16.28 GHz. Our analysis shows that the frequency and throughput of the RSFQ-based S-box are about four times higher than those of the CMOS-based S-box. Further, we design and fabricate a few typical modules of the S-box. Subsequent measurements demonstrate the correct functioning of the modules in both low and high frequencies up to 28.8 GHz.

Chinese Physics B•May 18, 2022•2 citations

An Area Efficient Superconducting Unary CNN Accelerator

Patricia Gonzalez-Guerrero, Kylie Huch, Nirmalendu Patra +2 more

In superconducting circuits, information is carried by ps-wide, µV-tall, Single Flux Quanta (SFQ) pulses. These circuits can operate at frequencies of hundreds of GHz with orders of magnitude lower switching energy than complementary-metal-oxide-semiconductors (CMOS). However, under the stringent area constraints of modern superconductor technologies, fully-fledged, CMOS-inspired superconducting architectures cannot be fabricated at large scales. Unary SFQ (U-SFQ) is an alternative computing paradigm that addresses these area constraints. In U-SFQ, information is mapped to a combination of streams of SFQ pulses and in the temporal domain. In this work, we propose a U-SFQ Convolutional Neural Network (CNN) hardware accelerator capable of comparable peak performance with state-of-the-art superconducting binary (B-SFQ) approaches in 32× less area. CNNs can operate with 5 to 8 bits of resolution with no significant degradation in classification accuracy. The proposed CNN accelerator effortlessly supports this variable resolution and, for less than 7 bits, yields 5×-63× better performance than CMOS and 15×-173× better area efficiency than B-SFQ.

International Symposium on Quality Electronic Design (ISQED)•Apr 5, 2023•3 citations

Temporal and SFQ pulse-streams encoding for area-efficient superconducting accelerators

Patricia Gonzalez-Guerrero, Meriam Gay Bautista, Darren Lyles +1 more

Superconducting technology is a prime candidate for the future of computing. However, current superconducting prototypes are limited to small-scale examples due to stringent area constraints and complex architectures inspired from voltage-level encoding in CMOS; this is at odds with the ps-wide Single Quantum Flux (SFQ) pulses used in superconductors to carry information. In this work, we propose a wave-pipelined Unary SFQ (U-SFQ) architecture that leverages the advantages of two data representations: pulse-streams and Race Logic (RL). We introduce novel building blocks such as multipliers, adders, and memory cells, which leverage the natural properties of SFQ pulses to mitigate area constraints. We then design and simulate three popular hardware accelerators: i) a Processing Element (PE), typically used in spatial architectures; ii) A dot-product-unit (DPU), one of the most popular accelerators in artificial neural networks and digital signal processing (DSP); and iii) A Finite Impulse Response (FIR) filter, a popular and computationally demanding DSP accelerator. The proposed U-SFQ building blocks require up to 200× fewer JJs compared to their SFQ binary counterparts, exposing an area-delay trade-off. This work mitigates the stringent area constraints of superconducting technology.

International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)•Feb 22, 2022•18 citations
This research explores the use of superconductor electronics (SCE) for accelerating fully homomorphic encryption (FHE), focusing on the Number-Theoretic Transform (NTT), a key computational bottleneck in FHE schemes. We present SCE-NTT, a dedicated hardware accelerator based on superconductive single flux quantum (SFQ) logic and memory, targeting high performance and energy efficiency beyond the limits of conventional CMOS. To address SFQ constraints such as limited dense RAM and restricted fanin/fanout, we propose a deeply pipelined NTT-128 architecture using shift register memory (SRM). Designed for N=128 32-bit coefficients, NTT-128 comprises log2(N)=7 processing elements (PEs), each featuring a butterfly unit (BU), dual coefficient memories operating in ping-pong mode via FIFO-based SRM queues, and twiddle factor buffers. The BU integrates a Shoup modular multiplier optimized for a small area, leveraging precomputed twiddle factors. A new RSFQ cell library with over 50 parameterized cells, including compound logic units, was developed for implementation. Functional and timing correctness were validated using JoSIM analog simulations and Verilog models. A multiphase clocking scheme was employed to enhance robustness and reduce path-balancing overhead, improving circuit reliability. Fabricated results show the NTT-128 unit achieves 531 million NTT/sec at 34 GHz, over 100x faster than state-of-the-art CMOS equivalents. We also project that the architecture can scale to larger sizes, such as a 2^14-point NTT in approximately 482 ns. Key-switch throughput is estimated at 1.63 million operations/sec, significantly exceeding existing hardware. These results demonstrate the strong potential of SCE-based accelerators for scalable, energy-efficient secure computation in the post-quantum era, with further gains anticipated through advances in fabrication.
The advent of quantum computing has engendered a widespread proliferation of efforts utilizing qubits for optimizing classical computational algorithms. Number Theoretic Transform (NTT) is one such popular algorithm that accelerates polynomial multiplication significantly and is consequently, the core arithmetic operation in most homomorphic encryption algorithms. Hence, fast and efficient execution of NTT is highly imperative for practical implementation of homomorphic encryption schemes in different computing paradigms. In this paper, we, for the first time, propose an efficient and scalable Quantum Number Theoretic Transform (QNTT) circuit using quantum gates. We introduce a novel exponential unit for modular exponential operation, which furnishes an algorithmic complexity of O(n). Our proposed methodology performs further optimization and logic synthesis of QNTT, that is significantly fast and facilitates efficient implementations on IBM’s quantum computers. The optimized QNTT achieves a gate-level complexity reduction from power of two to one with respect to bit length. Our methodology utilizes 44.2% fewer gates, thereby minimizing the circuit depth and a corresponding reduction in overhead and error probability, for a 4-point QNTT compared to its unoptimized counterpart.
Abstract Fast phase logic (FPL) is a novel digital superconductor electronic (SCE) logic family that employs multiple junction types, including switching 0-Josephson junctions (0-JJs), non-switching 0-JJ stacks, and π-JJs. FPL enables flexible, automatable cell layouts, faster pulse propagation, reduced bias current via phaseshifting π-JJs, and minimized inductive loops, thereby reducing susceptibility to trapped flux and crosstalk.A fabrication process to support FPL is proposed. NbTiN superconductors offer small grain sizes, smooth surfaces, and thermal stability up to 400 ○ C, while high-Jc, self-shunted JJs enable compact devices. AlN dielectrics provide good crystal matching to NbTiN, improving superconducting properties. Projections indicate that FPL, combined with the proposed process, can achieve a two-order-of-magnitude increase in integration density over conventional RSFQ logic and a five-fold reduction in supply current. The increased density reduces latency and improves computational throughput, while NbTiN-based devices provide higher output voltage and impedance, improving compatibility with CMOS circuits. Further fabrication advancements, such as higher-Jc NbTiN-based JJs, higher processing temperatures, and stacked JJ structures, could enhance FPL implementation and scalability toward very large-scale integration (VLSI). FPL has the potential to significantly advance SCE technology, with near-term applications in accelerator cores for signal processing and artificial intelligence, and long-term potential in supercomputing. Its advantages are evaluated through an architectural study of a fast Fourier transform (FFT) circuit, with comparisons to CMOS and SFQ technologies for digital electronics.
In this paper we present an FFT hardware accelerator based on a butterfly (BUT) structure implemented in Rapid Single-Flux-Quantum (RSFQ) superconducting computing. Our design uses a data representation that combines Race Logic (RL) with pulse streams in order to perform arithmetic operations and generate twiddle factors efficiently. Given today’s stringent area limitations for RSFQ technology, our design aims to maximize area efficiency. Our design shows 2 to 18× higher throughput per area, measured as performance per number of Josephson junctions. Moreover, we demonstrate 20-70% area savings compared to binary RSFQ FFT implementations for 2 to 64 N-points.
Rapid single flux quantum (RSFQ) circuits are a kind of superconducting digital circuits, having properties of a natural gate-level pipelining synchronous sequential circuit, which demonstrates high energy efficiency and high throughput advantage. We find that the high-throughput and high-speed performance of RSFQ circuits can take the advantage of a hardware implementation of the encryption algorithm, whereas these are rarely applied to this field. Among the available encryption algorithms, the advanced encryption standard (AES) algorithm is an advanced encryption standard algorithm. It is currently the most widely used symmetric cryptography algorithm. In this work, we aim to demonstrate the SubByte operation of an AES-128 algorithm using RSFQ circuits based on the SIMIT Nb03 process. We design an AES S-box circuit in the RSFQ logic, and compare its operational frequency, power dissipation, and throughput with those of the CMOS-based circuit post-simulated in the same structure. The complete RSFQ S-box circuit costs a total of 42237 Josephson junctions with nearly 130 Gbps throughput under the maximum simulated frequency of 16.28 GHz. Our analysis shows that the frequency and throughput of the RSFQ-based S-box are about four times higher than those of the CMOS-based S-box. Further, we design and fabricate a few typical modules of the S-box. Subsequent measurements demonstrate the correct functioning of the modules in both low and high frequencies up to 28.8 GHz.
In superconducting circuits, information is carried by ps-wide, µV-tall, Single Flux Quanta (SFQ) pulses. These circuits can operate at frequencies of hundreds of GHz with orders of magnitude lower switching energy than complementary-metal-oxide-semiconductors (CMOS). However, under the stringent area constraints of modern superconductor technologies, fully-fledged, CMOS-inspired superconducting architectures cannot be fabricated at large scales. Unary SFQ (U-SFQ) is an alternative computing paradigm that addresses these area constraints. In U-SFQ, information is mapped to a combination of streams of SFQ pulses and in the temporal domain. In this work, we propose a U-SFQ Convolutional Neural Network (CNN) hardware accelerator capable of comparable peak performance with state-of-the-art superconducting binary (B-SFQ) approaches in 32× less area. CNNs can operate with 5 to 8 bits of resolution with no significant degradation in classification accuracy. The proposed CNN accelerator effortlessly supports this variable resolution and, for less than 7 bits, yields 5×-63× better performance than CMOS and 15×-173× better area efficiency than B-SFQ.
Superconducting technology is a prime candidate for the future of computing. However, current superconducting prototypes are limited to small-scale examples due to stringent area constraints and complex architectures inspired from voltage-level encoding in CMOS; this is at odds with the ps-wide Single Quantum Flux (SFQ) pulses used in superconductors to carry information. In this work, we propose a wave-pipelined Unary SFQ (U-SFQ) architecture that leverages the advantages of two data representations: pulse-streams and Race Logic (RL). We introduce novel building blocks such as multipliers, adders, and memory cells, which leverage the natural properties of SFQ pulses to mitigate area constraints. We then design and simulate three popular hardware accelerators: i) a Processing Element (PE), typically used in spatial architectures; ii) A dot-product-unit (DPU), one of the most popular accelerators in artificial neural networks and digital signal processing (DSP); and iii) A Finite Impulse Response (FIR) filter, a popular and computationally demanding DSP accelerator. The proposed U-SFQ building blocks require up to 200× fewer JJs compared to their SFQ binary counterparts, exposing an area-delay trade-off. This work mitigates the stringent area constraints of superconducting technology.