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MicroCloud Hologram Inc. Efficient Deterministic Quantum State Preparation Algorithm Based on Decision Diagrams

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(High)
Rhea-AI Sentiment
(Positive)
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MicroCloud Hologram (NASDAQ: HOLO) announced an Efficient Deterministic Quantum State Preparation Algorithm that uses reduced Algebraic Decision Diagrams to map quantum-state structure to circuits. The method yields circuit complexity O(k·n) tied to reduced decision-diagram paths, claims deterministic fidelity 1 (ideal noise-free), and reports CNOT reductions of 86.61%99.9% for a Byzantine agreement initial state. The company also discloses cash reserves >$390M and plans to invest >$400M in blockchain, quantum R&D, and related technologies.

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Positive

  • Circuit complexity scales O(k·n) with decision-diagram paths
  • Deterministic preparation claims theoretical fidelity of 1 (noiseless)
  • CNOT gate reductions of 86.61%–99.9% for Byzantine initial state
  • Cash reserves exceed $390M
  • Planned >$400M investment in quantum, blockchain, and AI

Negative

  • None.

Key Figures

Cash reserves: over $390 million Planned investment: over $400 million
2 metrics
Cash reserves over $390 million Company-stated cash reserves in quantum/holography strategy description
Planned investment over $400 million Planned spend on blockchain, quantum computing, quantum holography, AI, AR and related R&D

Market Reality Check

Price: $1.6700 Vol: Volume 346,655 is about 0...
low vol
$1.6700 Last Close
Volume Volume 346,655 is about 0.49x the 20-day average of 712,368, indicating subdued trading interest pre-announcement. low
Technical Shares at $1.67 are trading below the 200-day MA of $3.45 and sit far under the $11.82 52-week high while slightly above the $1.535 52-week low.

Peers on Argus

HOLO was up about 2.45% while momentum-flagged peers like GAUZ and LINK were dow...
2 Down

HOLO was up about 2.45% while momentum-flagged peers like GAUZ and LINK were down 17.43% and 7.37%, suggesting a stock-specific move rather than a sector-wide shift.

Historical Context

5 past events · Latest: Apr 22 (Positive)
Pattern 5 events
Date Event Sentiment Move Catalyst
Apr 22 Quantum crypto R&D Positive +1.6% Announced R&D plan for Bitcoin quantum-resistant protocol and large planned investment.
Apr 16 Quantum auth scheme Positive +8.1% Detailed Grover-based quantum identity authentication schemes for multiple use cases.
Apr 15 Quantum simulator launch Positive +2.5% Launched FPGA-based surface code quantum simulation platform with performance claims.
Apr 14 Quantum 3D detection Positive +2.5% Announced hybrid quantum-classical 3D object technology using MC-QCNN for perception tasks.
Apr 10 Quantum-holographic auth Positive -4.1% Unveiled quantum-holographic authentication system with very low misidentification rate.
Pattern Detected

Recent quantum and blockchain R&D announcements have often coincided with positive next-day moves, with only one notable divergence on otherwise positive technology news.

Recent Company History

Over the past month, HOLO has repeatedly highlighted quantum and blockchain R&D: from a Bitcoin quantum-resistant protocol on Apr 22 to multiple quantum authentication, simulation, and AI-related technologies between Apr 10–16. These releases were generally followed by positive price reactions in the +1.62% to +8.13% range, except one decline after quantum-holographic authentication news. Today’s quantum state preparation algorithm extends this stream of technical milestones in quantum computing.

Market Pulse Summary

This announcement highlights HOLO’s continued push into quantum computing, introducing a determinist...
Analysis

This announcement highlights HOLO’s continued push into quantum computing, introducing a deterministic quantum state preparation algorithm that targets significant CNOT gate reductions for structured states. It extends a series of recent quantum and blockchain R&D updates following earlier authentication, simulation, and AI-related launches. With the stock trading near its 52-week low and below the 200-day MA, investors may watch whether repeated technical milestones eventually align with changes in financial performance or broader strategic disclosures.

Key Terms

cnot, quantum convolutional neural network, adas
3 terms
cnot technical
"decomposed into 4-6 CNOTs) and one MCX (also decomposed into O(n) CNOTs)"
CNOT is shorthand for the CCR4–NOT complex, a group of proteins inside cells that helps control how long messenger RNA messages last and therefore how much of a protein gets made. Investors should care because changes in CNOT activity can be linked to disease pathways and are studied as potential drug targets or biomarkers; think of it as a volume control for protein production that can affect the success of therapies or diagnostics.
quantum convolutional neural network technical
"Multi-Channel Quantum Convolutional Neural Network (MC-QCNN)"
A quantum convolutional neural network is an advanced computer system that uses principles of quantum physics to analyze complex data more efficiently than traditional methods. It mimics how the brain recognizes patterns but operates at a level that could process vast amounts of information rapidly, potentially uncovering insights that help investors make better decisions. Its development could lead to faster, more accurate predictions in financial markets.
adas technical
"providing services to customers offering holographic advanced driving assistance systems (ADAS)"
Advanced Driver Assistance Systems (ADAS) are electronic systems in vehicles that assist the driver with safety tasks. Examples include automatic emergency braking, lane keeping assist, and adaptive cruise control. These systems use sensors and cameras to improve vehicle safety.

AI-generated analysis. Not financial advice.

SHENZHEN, China, May 04, 2026 (GLOBE NEWSWIRE) -- MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the "Company"), a technology service provider, announces the launch of its latest core technology — the Efficient Deterministic Quantum State Preparation Algorithm Based on Decision Diagrams. This original algorithm, for the first time, systematically applies the highly mature Decision Diagram data structure from classical computing to the precise representation and circuit synthesis of quantum states. By cleverly exploiting the path reduction and sharing characteristics of decision diagrams, it establishes a strict linear relationship between the number of CNOT gates in the preparation circuit and the number of reduced paths in the decision diagram. This achieves significant resource compression for highly structured quantum states, markedly surpassing existing mainstream methods, and injects strong momentum into the foundational operation layer of practical quantum computing.

As a general-purpose data structure, the decision diagram was originally born in the field of representation and analysis of classical Boolean functions. It is essentially a directed acyclic graph that compactly encodes the truth table of a function through variable nodes, 0/1 branch edges, and terminal nodes, avoiding exponential storage explosion. In its simplified or reduced form, the decision diagram achieves extremely high compression rates by merging identical subgraphs, eliminating redundant nodes, and sharing paths. HOLO extends this classical tool to the quantum domain and proposes a class of quantum states that can be efficiently represented using reduced Algebraic Decision Diagrams (ADD). Specifically, for a quantum state |ϕ⟩ = ∑ α_s |s⟩, where s is an n-bit basis state string, the non-zero amplitudes α_s correspond to paths in the decision diagram. Each internal node represents a qubit variable, solid edges represent the 1-branch, dashed edges represent the 0-branch, and terminal nodes store the normalized amplitude values. Through reduction rules — merging identical terminals, eliminating single-child nodes, and sharing identical subgraphs — a state that might originally have m non-zero amplitudes is compressed into a decision diagram with only k paths, where k is often much smaller than m or even 2^n. This representation captures the sparsity and repetitive patterns of the quantum state, such as certain subsets of basis states sharing the same amplitude or substructure, thereby providing a roadmap for subsequent circuit construction.

Based on this decision diagram representation, the core of the algorithm developed by HOLO lies in directly utilizing the structure of the graph to construct quantum circuits, rather than blindly enumerating all basis states. The algorithm employs a single auxiliary qubit (initialized to |1⟩, acting as an unprocessed flag), with data qubits initialized to |0⟩. The entire process is completely deterministic, requiring no measurements or random post-selection. The algorithm first traverses the decision diagram in post-order, computes the local probability p_0 for each node (based on the sum of squared amplitudes of child nodes, with reduced nodes multiplied by a 2^e factor), and precomputes the corresponding Ry rotation gate G(p_0), which rotates |0⟩ to √p_0|0⟩ + √(1-p_0)|1⟩, encoding the branch probability entering that subtree. It then proceeds to pre-order traversal, recursively constructing the circuit starting from the root node: for branch nodes, it applies a doubly-controlled G(p_0) gate (controlled by the auxiliary qubit and the nearest |1⟩ in the path) to the current qubit; for single-child nodes, it inserts a doubly-controlled X gate (CNOT) and handles half-probability rotations between reduced nodes; when reaching a terminal, it first applies a phase gate e^{i arg(α)} to adjust the amplitude phase, and finally uses a multi-controlled X gate (MCX, controlled by all branch nodes in the path) to flip the auxiliary qubit, marking it as |0⟩ (processed). This design ensures that subsequent path preparation does not interfere with previously completed paths, as the auxiliary qubit acts as a protection switch on the processed subspace.

The key to the technical implementation logic lies in the sharing and sequential processing of decision diagram paths. HOLO cleverly sorts the paths in descending binary value order (p1 ≻ p2 ≻ ... ≻ pk), so that each time construction continues from the last common prefix node of the previous path, avoiding redundant operations. The number of gates contributed by each path is at most O(n), including n doubly-controlled gates (decomposed into 4-6 CNOTs) and one MCX (also decomposed into O(n) CNOTs). However, due to prefix sharing and node reduction, the actual total circuit complexity is only O(kn), i.e., linearly related to the number of paths k in the decision diagram, rather than to the number of non-zero amplitudes m or 2^n. This forms a sharp contrast with traditional methods: general preparation often requires O(m n) or even more gates and struggles to exploit structure; although early decision diagram-based methods offered some compression, they did not fully exploit path marking and reduction, resulting in higher gate counts. HOLO’s algorithm ensures a clean circuit and theoretically achieves a fidelity of 1 (under ideal noiseless conditions) through the auxiliary qubit’s path-locking mechanism and precise probability/phase injection, truly realizing deterministic preparation.

It is particularly worth mentioning that the algorithm demonstrates extreme performance on the initial state of the quantum Byzantine agreement protocol. The quantum Byzantine agreement protocol is a key protocol for achieving consensus in distributed quantum computing, and its initial quantum state often exhibits a highly sparse decision diagram with specific shared substructures. HOLO’s experiments show that for the initial state of this protocol, the reduction in the number of CNOT gates ranges from 86.61% to 99.9%. This means that in actual multi-party quantum networks, the resource overhead in the protocol initialization phase is significantly reduced, with higher fidelity, thereby improving the overall reliability and scalability of the protocol. This application directly proves the practical value of the technology.

Looking back at the development history of quantum computing, from Shor’s algorithm to Grover’s search, and then to variational quantum eigensolvers (VQE), every advancement has relied on efficient fundamental primitives. As the data entry point, the optimization of quantum state preparation directly affects the performance of the entire stack. HOLO’s algorithm not only fills the gap in structured quantum state preparation but also lays the foundation for broader applications of decision diagrams in the quantum field — which may expand to areas such as quantum circuit synthesis and simulation acceleration in the future. As the scale of qubits advances toward hundreds or even thousands, the exponential barrier of general state preparation will become increasingly prominent. HOLO’s algorithm provides a clear path: as long as the problem has a structure that can be represented by a decision diagram, efficient injection can be achieved.

This is not only a demonstration of HOLO’s R&D strength but also a milestone event in the quantum computing ecosystem. In today’s fiercely competitive global quantum race, achieving a quantum breakthrough in the classical field of decision diagrams demonstrates a powerful capability to transform from theory to engineering. Quantum Decision Innovation Co., Ltd. will continue to delve deeply, committed to promoting this technology to more application scenarios. This algorithm will help quantum computers truly solve real-world problems and drive the comprehensive transformation of human computing paradigms into the quantum era.

About MicroCloud Hologram Inc.

MicroCloud Hologram Inc. (NASDAQ: HOLO) is committed to the research and development and application of holographic technology. Its holographic technology services include holographic light detection and ranging (LiDAR) solutions based on holographic technology, holographic LiDAR point cloud algorithm architecture design, technical holographic imaging solutions, holographic LiDAR sensor chip design, and holographic vehicle intelligent vision technology, providing services to customers offering holographic advanced driving assistance systems (ADAS). MicroCloud Hologram Inc. provides holographic technology services to global customers. MicroCloud Hologram Inc. also provides holographic digital twin technology services and owns proprietary holographic digital twin technology resource libraries. Its holographic digital twin technology resource library utilizes a combination of holographic digital twin software, digital content, space data-driven data science, holographic digital cloud algorithms, and holographic 3D capture technology to capture shapes and objects in 3D holographic form.

MicroCloud Hologram Inc. focuses on the development of quantum computing and quantum holography. With cash reserves exceeding 390 million USD, the company plans to invest over 400 million USD in blockchain development, quantum computing R&D, quantum holography technology, as well as in the development of derivatives and technologies in cutting-edge fields such as AI, AR, and more. MicroCloud Hologram Inc.'s goal is to become a global leader in quantum holography and quantum computing technologies.

Safe Harbor Statement

This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as "may," "will," "intend," "should," "believe," "expect," "anticipate," "project," "estimate," or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company's expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company's goals and strategies; the Company's future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission ("SEC"), including the Company's most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company's filings with the SEC, which are available for review at www.sec.gov. The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof.

Contacts

MicroCloud Hologram Inc.
Email: IR@mcvrar.com


FAQ

What is HOLO's new quantum state preparation algorithm (HOLO)?

HOLO introduced an algorithm using reduced Algebraic Decision Diagrams to build circuits deterministically. According to company, it maps diagram paths to gates, creating circuits with complexity O(k·n) tied to reduced-path count rather than 2^n.

How much circuit compression does HOLO report for Byzantine agreement initial states (HOLO)?

HOLO reports CNOT reductions ranging from 86.61% to 99.9% for that initial state. According to company, high sparsity and shared substructures in the decision diagram drive these measured gate-count reductions.

Does HOLO's method guarantee perfect state preparation fidelity (HOLO)?

The company claims theoretical fidelity of 1 under ideal, noiseless conditions for the constructed circuits. According to company, an auxiliary qubit and precise rotations/phases enable deterministic, measurement-free preparation in that model.

What resources and investments did MicroCloud Hologram disclose (HOLO)?

MicroCloud Hologram discloses cash reserves exceeding $390M and plans to invest over $400M in blockchain, quantum R&D, and related fields. According to company, these funds support quantum computing and quantum holography development.

How does HOLO's algorithm compare to traditional general-state preparation (HOLO)?

HOLO states its approach avoids enumerating all basis states and leverages path sharing, yielding O(k·n) gates versus O(m·n) or worse for general methods. According to company, this more efficiently exploits structured state patterns encoded by decision diagrams.